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• Journal article
  Wairagkar M, De Lima MR, Harrison M, Batey P, Daniels S, Barnaghi P, Sharp DJ, Vaidyanathan Ret al., 2021,

  Conversational artificial intelligence and affective social robot for monitoring health and well-being of people with dementia.

  , Alzheimers & Dementia, Vol: 17 Suppl 11, Pages: e053276-e053276, ISSN: 1552-5260

  BACKGROUND: Social robots are anthropomorphised platforms developed to interact with humans, using natural language, offering an accessible and intuitive interface suited to diverse cognitive abilities. Social robots can be used to support people with dementia (PwD) and carers in their homes managing medication, hydration, appointments, and evaluating mood, wellbeing, and potentially cognitive decline. Such robots have potential to reduce care burden and prolong independent living, yet translation into PwD use remains insignificant. METHOD: We have developed two social robots - a conversational robot and a digital social robot for mobile devices capable of communicating through natural language (powered by Amazon Alexa) and facial expressions that ask PwD daily questions about their health and wellbeing and also provide digital assistant functionality. We record data comprising of PwD's responses to daily questions, audio speech and text of conversations with Alexa to automatically monitor their health and wellbeing using machine learning. We followed user-centric development processes by conducting focus groups with 13 carers, 2 PwD and 5 clinicians to iterate the design. We are testing social robot with 3 PwD in their homes for ten weeks. RESULT: We received positive feedback on social robot from focus group participants. Ease of use, low maintenance, accessibility, assistance with medication, supporting with health and wellbeing were identified as the key opportunities for social robots. Based on responses to a daily questionnaire, our robots generate a report detailing PwD wellbeing that is automatically sent via email to family members or carers. This information is also stored systematically in a database that can help clinicians monitor their patients remotely. We use natural language processing to analyse conversations and identify topics of interest to PwD such that robot behaviour could be adapted. We process speech using signal processing and machine lear

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• Journal article
  Rezvani R, Kouchaki S, Nilforooshan R, Sharp DJ, Barnaghi Pet al., 2021,

  Analysing behavioural changes in people with dementia using in-home monitoring technologies.

  , Alzheimers & Dementia, Vol: 17 Suppl 11, Pages: e052181-e052181, ISSN: 1552-5260

  BACKGROUND: Behavioural changes and neuropsychiatric symptoms such as agitation are common in people with dementia. These symptoms impact the quality of life of people with dementia and can increase the stress on caregivers. This study aims to identify the likelihood of having agitation in people affected by dementia (i.e., patients and carers) using routinely collected data from in-home monitoring technologies. We have used a digital platform and analytical methods, developed in our previous study, to generate alerts when changes occur in the digital markers collected using in-home sensing technologies (i.e., vital signs, environmental and activity data). A care monitoring team use the platform and interact with participants and caregivers when an alert is generated. METHOD: We have used connected sensory devices to collect environmental markers, including Passive Infra-Red (PIR), smart power plugs for monitoring home appliance use, motion and door sensors. The environmental marker data have been aggregated within each hour and used to train an agitation risk analysis model. We have trained a model using data collected from 88 homes (∼6 months of data from each home). The proposed model has two components: a self-supervised transformation learning and an ensemble classification model for agitation likelihood. Ten different neural network encoders are learned to create pseudo-labels using the samples from the unlabelled data. We use these pseudo-labels to train a classification model with a convolutional block and a decision layer. The trained convolutional block is then used to learn a latent representation of the data for an ensemble classification block. RESULTS: Comparing with baseline models such as LSTM network, Bidirectional LSTM (BiLSTM) network, VGG, ResNet, Inception, Random Forest (RF), Support Vector Machine (SVM) and Gaussian Process (GP) classifiers, the proposed model performs better in sensitivity (recall) and area under the precision-recall curv

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• Journal article
  Liu KY, Howard R, Banerjee S, Comas-Herrera A, Goddard J, Knapp M, Livingston G, Manthorpe J, O'Brien JT, Paterson RW, Robinson L, Rossor M, Rowe JB, Sharp DJ, Sommerlad A, Suarez-Gonzalez A, Burns Aet al., 2021,

  Dementia wellbeing and COVID-19: Review and expert consensus on current research and knowledge gaps

  , INTERNATIONAL JOURNAL OF GERIATRIC PSYCHIATRY, Vol: 36, Pages: 1597-1639, ISSN: 0885-6230
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• Journal article
  Ahmadi N, Constandinou T, Bouganis C, 2021,

  Inferring entire spiking activity from local field potentials

  , Scientific Reports, Vol: 11, Pages: 1-13, ISSN: 2045-2322

  Extracellular recordings are typically analysed by separating them into two distinct signals: local field potentials (LFPs) andspikes. Previous studies have shown that spikes, in the form of single-unit activity (SUA) or multiunit activity (MUA), can beinferred solely from LFPs with moderately good accuracy. SUA and MUA are typically extracted via threshold-based techniquewhich may not be reliable when the recordings exhibit a low signal-to-noise ratio (SNR). Another type of spiking activity, referredto as entire spiking activity (ESA), can be extracted by a threshold-less, fast, and automated technique and has led to betterperformance in several tasks. However, its relationship with the LFPs has not been investigated. In this study, we aim toaddress this issue by inferring ESA from LFPs intracortically recorded from the motor cortex area of three monkeys performingdifferent tasks. Results from long-term recording sessions and across subjects revealed that ESA can be inferred from LFPswith good accuracy. On average, the inference performance of ESA was consistently and significantly higher than those of SUAand MUA. In addition, local motor potential (LMP) was found to be the most predictive feature. The overall results indicate thatLFPs contain substantial information about spiking activity, particularly ESA. This could be useful for understanding LFP-spikerelationship and for the development of LFP-based BMIs.

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• Journal article
  Huo W, Moon H, Alouane MA, Bonnet V, Huang J, Amirat Y, Vaidyanathan R, Mohammed Set al., 2022,

  Impedance modulation control of a lower limb exoskeleton to assist sit-to-stand movements

  , IEEE Transactions on Robotics, Vol: 38, Pages: 1230-1249, ISSN: 1552-3098

  As an important movement of the daily living activities, sit-to-stand (STS) movement is usually a difficult task facingelderly and dependent people. In this article, a novel impedancemodulation strategy of a lower limb exoskeleton is proposed toprovide appropriate power and balance assistance during STSmovements while preserving the wearer’s control priority. Theimpedance modulation control strategy ensures adaptation of themechanical impedance of the human-exoskeleton system towardsa desired one requiring less wearer’s effect while reinforcing thewearer’s balance control ability during STS movements. A humanjoint torque observer is designed to estimate the joint torquesdeveloped by the wearer using joint position kinematics instead ofelectromyography (EMG) or force sensors; a time-varying desiredimpedance model is proposed according to the wearer’s lowerlimb motion ability. A virtual environmental force is designedfor the balance reinforcement control. Stability and robustness ofthe proposed method are theoretically analyzed. Simulations wereimplemented to illustrate the characteristics and performance ofthe proposed approach. Experiments with four healthy subjectswere carried out to evaluate the effectiveness of the proposedmethod and show satisfactory results in terms of appropriatepower assist and balance reinforcement.

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